{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用pandas进行数据分析\n",
    "* a.读取数据集 (可选择 独角兽数据、国家数据库数据或者其他的具有一定数据分析价值的数据集 )\n",
    "* b.数据清洗、数据筛选、空缺值处理等\n",
    "* c.数据的统计计算（分组、聚合、重塑）\n",
    "* d.数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>蚂蚁金服</td>\n",
       "      <td>Ant Financial</td>\n",
       "      <td>10000</td>\n",
       "      <td>中国</td>\n",
       "      <td>杭州</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>井贤栋</td>\n",
       "      <td>2014</td>\n",
       "      <td>春华资本、中投海外、红杉资本</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>Bytedance</td>\n",
       "      <td>5000</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>Didi Chuxing</td>\n",
       "      <td>3600</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>程维</td>\n",
       "      <td>2012</td>\n",
       "      <td>腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>陆金所</td>\n",
       "      <td>Lufax</td>\n",
       "      <td>2700</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>计葵生</td>\n",
       "      <td>2011</td>\n",
       "      <td>摩根士丹利、中银集团、国泰君安（香港）</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>微众银行</td>\n",
       "      <td>WeBank</td>\n",
       "      <td>1500</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>顾敏</td>\n",
       "      <td>2014</td>\n",
       "      <td>腾讯、华平投资、淡马锡</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>264</td>\n",
       "      <td>有利网</td>\n",
       "      <td>Yooli</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>吴逸然</td>\n",
       "      <td>2012</td>\n",
       "      <td>高瓴资本、晨兴资本、软银中国</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>264</td>\n",
       "      <td>网易有道</td>\n",
       "      <td>Youdao</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>软件与服务</td>\n",
       "      <td>周枫</td>\n",
       "      <td>2007</td>\n",
       "      <td>君联资本、慕华投资</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>264</td>\n",
       "      <td>云鸟科技</td>\n",
       "      <td>Yunniao</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>物流</td>\n",
       "      <td>韩毅</td>\n",
       "      <td>2014</td>\n",
       "      <td>华平投资、红杉资本、经纬中国</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>264</td>\n",
       "      <td>掌门1对1</td>\n",
       "      <td>Zhangmen</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>教育科技</td>\n",
       "      <td>张翼</td>\n",
       "      <td>2014</td>\n",
       "      <td>顺为资本、达晨创投、华平投资</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>264</td>\n",
       "      <td>转转</td>\n",
       "      <td>Zhuanzhuan</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>姚劲波</td>\n",
       "      <td>2015</td>\n",
       "      <td>腾讯</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>206 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      排名   企业名称   Company Name  估值（亿人民币）  国家  城市     行业 掌门人/创始人  成立年份  \\\n",
       "0      1   蚂蚁金服  Ant Financial     10000  中国  杭州   金融科技     井贤栋  2014   \n",
       "1      2   字节跳动      Bytedance      5000  中国  北京  媒体和娱乐     张一鸣  2012   \n",
       "2      3   滴滴出行   Didi Chuxing      3600  中国  北京   共享经济      程维  2012   \n",
       "3      6    陆金所          Lufax      2700  中国  上海   金融科技     计葵生  2011   \n",
       "4     11   微众银行         WeBank      1500  中国  深圳   金融科技      顾敏  2014   \n",
       "..   ...    ...            ...       ...  ..  ..    ...     ...   ...   \n",
       "201  264    有利网          Yooli        70  中国  北京   金融科技     吴逸然  2012   \n",
       "202  264   网易有道         Youdao        70  中国  北京  软件与服务      周枫  2007   \n",
       "203  264   云鸟科技        Yunniao        70  中国  北京     物流      韩毅  2014   \n",
       "204  264  掌门1对1       Zhangmen        70  中国  上海   教育科技      张翼  2014   \n",
       "205  264     转转     Zhuanzhuan        70  中国  北京   电子商务     姚劲波  2015   \n",
       "\n",
       "                     部分投资机构   region  \n",
       "0            春华资本、中投海外、红杉资本  环杭州湾大湾区  \n",
       "1       红杉资本、海纳亚洲、纪源资本、启明创投    渤海大湾区  \n",
       "2    腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本    渤海大湾区  \n",
       "3       摩根士丹利、中银集团、国泰君安（香港）  环杭州湾大湾区  \n",
       "4               腾讯、华平投资、淡马锡   粤港澳大湾区  \n",
       "..                      ...      ...  \n",
       "201          高瓴资本、晨兴资本、软银中国    渤海大湾区  \n",
       "202               君联资本、慕华投资    渤海大湾区  \n",
       "203          华平投资、红杉资本、经纬中国    渤海大湾区  \n",
       "204          顺为资本、达晨创投、华平投资  环杭州湾大湾区  \n",
       "205                      腾讯    渤海大湾区  \n",
       "\n",
       "[206 rows x 11 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv (\"hurun.csv\", encoding = \"utf8\", sep=\"\\t\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>字节跳动</td>\n",
       "      <td>Bytedance</td>\n",
       "      <td>5000</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张一鸣</td>\n",
       "      <td>2012</td>\n",
       "      <td>红杉资本、海纳亚洲、纪源资本、启明创投</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>滴滴出行</td>\n",
       "      <td>Didi Chuxing</td>\n",
       "      <td>3600</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>程维</td>\n",
       "      <td>2012</td>\n",
       "      <td>腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12</td>\n",
       "      <td>京东数科</td>\n",
       "      <td>JD Digits</td>\n",
       "      <td>1300</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>陈生强</td>\n",
       "      <td>2013</td>\n",
       "      <td>红杉资本、嘉实投资、中国太平</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14</td>\n",
       "      <td>快手</td>\n",
       "      <td>Kuaishou</td>\n",
       "      <td>1200</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>宿华</td>\n",
       "      <td>2011</td>\n",
       "      <td>红杉资本、晨兴资本、百度、腾讯</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20</td>\n",
       "      <td>比特大陆</td>\n",
       "      <td>Bitmain</td>\n",
       "      <td>800</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>区块链</td>\n",
       "      <td>詹克团，吴忌寒</td>\n",
       "      <td>2013</td>\n",
       "      <td>红杉资本、IDG、Crimson Ventures, 创新工场</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>264</td>\n",
       "      <td>易久批</td>\n",
       "      <td>Yijiupi</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>王朝成</td>\n",
       "      <td>2014</td>\n",
       "      <td>美团点评、腾讯、贝塔斯曼</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>264</td>\n",
       "      <td>有利网</td>\n",
       "      <td>Yooli</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>吴逸然</td>\n",
       "      <td>2012</td>\n",
       "      <td>高瓴资本、晨兴资本、软银中国</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>264</td>\n",
       "      <td>网易有道</td>\n",
       "      <td>Youdao</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>软件与服务</td>\n",
       "      <td>周枫</td>\n",
       "      <td>2007</td>\n",
       "      <td>君联资本、慕华投资</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>264</td>\n",
       "      <td>云鸟科技</td>\n",
       "      <td>Yunniao</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>物流</td>\n",
       "      <td>韩毅</td>\n",
       "      <td>2014</td>\n",
       "      <td>华平投资、红杉资本、经纬中国</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>264</td>\n",
       "      <td>转转</td>\n",
       "      <td>Zhuanzhuan</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>北京</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>姚劲波</td>\n",
       "      <td>2015</td>\n",
       "      <td>腾讯</td>\n",
       "      <td>渤海大湾区</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      排名  企业名称  Company Name  估值（亿人民币）  国家  城市     行业  掌门人/创始人  成立年份  \\\n",
       "1      2  字节跳动     Bytedance      5000  中国  北京  媒体和娱乐      张一鸣  2012   \n",
       "2      3  滴滴出行  Didi Chuxing      3600  中国  北京   共享经济       程维  2012   \n",
       "6     12  京东数科     JD Digits      1300  中国  北京   金融科技      陈生强  2013   \n",
       "7     14    快手      Kuaishou      1200  中国  北京  媒体和娱乐       宿华  2011   \n",
       "9     20  比特大陆       Bitmain       800  中国  北京    区块链  詹克团，吴忌寒  2013   \n",
       "..   ...   ...           ...       ...  ..  ..    ...      ...   ...   \n",
       "198  264   易久批       Yijiupi        70  中国  北京   电子商务      王朝成  2014   \n",
       "201  264   有利网         Yooli        70  中国  北京   金融科技      吴逸然  2012   \n",
       "202  264  网易有道        Youdao        70  中国  北京  软件与服务       周枫  2007   \n",
       "203  264  云鸟科技       Yunniao        70  中国  北京     物流       韩毅  2014   \n",
       "205  264    转转    Zhuanzhuan        70  中国  北京   电子商务      姚劲波  2015   \n",
       "\n",
       "                              部分投资机构 region  \n",
       "1                红杉资本、海纳亚洲、纪源资本、启明创投  渤海大湾区  \n",
       "2             腾讯、阿里巴巴、红杉资本、经纬中国、纪源资本  渤海大湾区  \n",
       "6                     红杉资本、嘉实投资、中国太平  渤海大湾区  \n",
       "7                    红杉资本、晨兴资本、百度、腾讯  渤海大湾区  \n",
       "9    红杉资本、IDG、Crimson Ventures, 创新工场  渤海大湾区  \n",
       "..                               ...    ...  \n",
       "198                     美团点评、腾讯、贝塔斯曼  渤海大湾区  \n",
       "201                   高瓴资本、晨兴资本、软银中国  渤海大湾区  \n",
       "202                        君联资本、慕华投资  渤海大湾区  \n",
       "203                   华平投资、红杉资本、经纬中国  渤海大湾区  \n",
       "205                               腾讯  渤海大湾区  \n",
       "\n",
       "[82 rows x 11 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_北京=df[df['城市'].str.contains('北京')]\n",
    "display(df_北京)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>陆金所</td>\n",
       "      <td>Lufax</td>\n",
       "      <td>2700</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>计葵生</td>\n",
       "      <td>2011</td>\n",
       "      <td>摩根士丹利、中银集团、国泰君安（香港）</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>25</td>\n",
       "      <td>平安医保科技</td>\n",
       "      <td>Ping An Healthcare Technology</td>\n",
       "      <td>600</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>健康科技</td>\n",
       "      <td>高菁</td>\n",
       "      <td>2016</td>\n",
       "      <td>IDG、思佰益、软银海外</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>34</td>\n",
       "      <td>金融壹账通</td>\n",
       "      <td>OneConnect</td>\n",
       "      <td>500</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>叶望春</td>\n",
       "      <td>2015</td>\n",
       "      <td>IDG、思佰益</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>57</td>\n",
       "      <td>达达-京东到家</td>\n",
       "      <td>New Dada</td>\n",
       "      <td>300</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>物流</td>\n",
       "      <td>蒯佳祺</td>\n",
       "      <td>2014</td>\n",
       "      <td>红杉资本、DST、京东</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>57</td>\n",
       "      <td>联影医疗</td>\n",
       "      <td>United Imaging</td>\n",
       "      <td>300</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>健康科技</td>\n",
       "      <td>薛敏</td>\n",
       "      <td>2010</td>\n",
       "      <td>中国人寿、国投创新</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>57</td>\n",
       "      <td>威马汽车</td>\n",
       "      <td>WM Motor</td>\n",
       "      <td>300</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>新能源汽车</td>\n",
       "      <td>沈晖</td>\n",
       "      <td>2015</td>\n",
       "      <td>远景能源、红杉资本、海纳亚洲</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>84</td>\n",
       "      <td>银联商务</td>\n",
       "      <td>China UMS</td>\n",
       "      <td>200</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>田林</td>\n",
       "      <td>2002</td>\n",
       "      <td>光际资本、IDG</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>84</td>\n",
       "      <td>哈啰出行</td>\n",
       "      <td>Hellobike</td>\n",
       "      <td>200</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>共享经济</td>\n",
       "      <td>杨磊</td>\n",
       "      <td>2016</td>\n",
       "      <td>纪源资本、磐谷创投、愉悦资本、蚂蚁金服</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>84</td>\n",
       "      <td>复宏汉霖</td>\n",
       "      <td>Henlius</td>\n",
       "      <td>200</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>生命科学</td>\n",
       "      <td>刘世高</td>\n",
       "      <td>2009</td>\n",
       "      <td>华盖资本</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>84</td>\n",
       "      <td>喜马拉雅</td>\n",
       "      <td>Himalaya</td>\n",
       "      <td>200</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>余建军</td>\n",
       "      <td>2012</td>\n",
       "      <td>海纳亚洲、Sierra Ventures、前海兴旺</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>84</td>\n",
       "      <td>小红书</td>\n",
       "      <td>Xiaohongshu</td>\n",
       "      <td>200</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>软件与服务</td>\n",
       "      <td>毛文超</td>\n",
       "      <td>2013</td>\n",
       "      <td>红杉资本、腾讯、真格基金、纪源资本、阿里巴巴</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>84</td>\n",
       "      <td>易果生鲜</td>\n",
       "      <td>Yiguo</td>\n",
       "      <td>200</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>金光磊</td>\n",
       "      <td>2005</td>\n",
       "      <td>阿里巴巴、高盛</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>84</td>\n",
       "      <td>游侠汽车</td>\n",
       "      <td>Youxia</td>\n",
       "      <td>200</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>新能源汽车</td>\n",
       "      <td>卫俊</td>\n",
       "      <td>2014</td>\n",
       "      <td>前海梧桐、中创海洋</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>138</td>\n",
       "      <td>爱回收</td>\n",
       "      <td>Aihuishou</td>\n",
       "      <td>150</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>软件与服务</td>\n",
       "      <td>陈雪峰</td>\n",
       "      <td>2010</td>\n",
       "      <td>京东、凯辉基金、达晨创投、天图资本、晨兴资本</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>138</td>\n",
       "      <td>灿星</td>\n",
       "      <td>Canxing</td>\n",
       "      <td>150</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>田明</td>\n",
       "      <td>2006</td>\n",
       "      <td>阿里巴巴、腾讯、华人文化</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>138</td>\n",
       "      <td>依图科技</td>\n",
       "      <td>YITU</td>\n",
       "      <td>150</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>朱珑</td>\n",
       "      <td>2012</td>\n",
       "      <td>云锋基金、红杉资本、真格基金、高瓴资本</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>224</td>\n",
       "      <td>爱驰汽车</td>\n",
       "      <td>Aiways</td>\n",
       "      <td>100</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>新能源汽车</td>\n",
       "      <td>谷峰</td>\n",
       "      <td>2017</td>\n",
       "      <td>腾讯、沙钢集团、明驰基金</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>224</td>\n",
       "      <td>高顿</td>\n",
       "      <td>Gaodun</td>\n",
       "      <td>100</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>教育科技</td>\n",
       "      <td>李锋</td>\n",
       "      <td>2006</td>\n",
       "      <td>前程无忧</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>224</td>\n",
       "      <td>界面</td>\n",
       "      <td>Jiemian</td>\n",
       "      <td>100</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>何力</td>\n",
       "      <td>2014</td>\n",
       "      <td>昆仑信托</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>224</td>\n",
       "      <td>驴妈妈</td>\n",
       "      <td>lvmama</td>\n",
       "      <td>100</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>洪清华</td>\n",
       "      <td>2008</td>\n",
       "      <td>红杉资本、鼎晖投资</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>224</td>\n",
       "      <td>魔方公寓</td>\n",
       "      <td>Mofang</td>\n",
       "      <td>100</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>房地产科技</td>\n",
       "      <td>葛岚</td>\n",
       "      <td>2010</td>\n",
       "      <td>中航信托、华平投资、德同资本</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>224</td>\n",
       "      <td>途虎养车</td>\n",
       "      <td>Tuhu</td>\n",
       "      <td>100</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>陈敏</td>\n",
       "      <td>2014</td>\n",
       "      <td>高瓴资本、启明创投、君联资本、红杉资本</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>224</td>\n",
       "      <td>微鲸</td>\n",
       "      <td>Whaley</td>\n",
       "      <td>100</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>消费品</td>\n",
       "      <td>黎瑞刚</td>\n",
       "      <td>2015</td>\n",
       "      <td>腾讯、华人文化产业基金</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>224</td>\n",
       "      <td>药明明码</td>\n",
       "      <td>WuXi NextCODE</td>\n",
       "      <td>100</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>生命科学</td>\n",
       "      <td>李革</td>\n",
       "      <td>2015</td>\n",
       "      <td>淡马锡、红杉资本、云锋基金、</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>224</td>\n",
       "      <td>找钢网</td>\n",
       "      <td>Zhaogang</td>\n",
       "      <td>100</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>王东</td>\n",
       "      <td>2012</td>\n",
       "      <td>IDG、经纬中国、红杉资本、真格基金</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>264</td>\n",
       "      <td>一起作业</td>\n",
       "      <td>17zuoye</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>教育科技</td>\n",
       "      <td>刘畅</td>\n",
       "      <td>2007</td>\n",
       "      <td>真格基金、顺为资本、老虎基金</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>264</td>\n",
       "      <td>阿里体育</td>\n",
       "      <td>Alisports</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>张勇</td>\n",
       "      <td>2015</td>\n",
       "      <td>云峰基金、太平资产</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>264</td>\n",
       "      <td>安能物流</td>\n",
       "      <td>Ane</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>物流</td>\n",
       "      <td>王拥军</td>\n",
       "      <td>2010</td>\n",
       "      <td>红杉资本、凯雷投资、高盛、华平投资</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>264</td>\n",
       "      <td>安翰医疗</td>\n",
       "      <td>Ankon</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>健康科技</td>\n",
       "      <td>吉朋松</td>\n",
       "      <td>2008</td>\n",
       "      <td>软银中国、大中投资</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>264</td>\n",
       "      <td>斑马网络</td>\n",
       "      <td>Banma</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>郝飞</td>\n",
       "      <td>2015</td>\n",
       "      <td>国投创新、云锋基金、尚颀资本</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>264</td>\n",
       "      <td>波奇网</td>\n",
       "      <td>Boqii</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>唐颖之</td>\n",
       "      <td>2012</td>\n",
       "      <td>高盛、集富亚洲、点亮资本</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>264</td>\n",
       "      <td>哒哒英语</td>\n",
       "      <td>DaDa</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>教育科技</td>\n",
       "      <td>郅慧</td>\n",
       "      <td>2013</td>\n",
       "      <td>老虎基金、华平资本、好未来</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>264</td>\n",
       "      <td>点融网</td>\n",
       "      <td>Dianrong</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>苏海德</td>\n",
       "      <td>2012</td>\n",
       "      <td>老虎环球基金、北极光创投、GIC</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>264</td>\n",
       "      <td>DotC United</td>\n",
       "      <td>DotC United</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>大数据</td>\n",
       "      <td>石一</td>\n",
       "      <td>2015</td>\n",
       "      <td>高榕资本、光速中国、晨兴资本</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>264</td>\n",
       "      <td>远景能源</td>\n",
       "      <td>Envision</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>新能源</td>\n",
       "      <td>张雷</td>\n",
       "      <td>2008</td>\n",
       "      <td>红杉资本</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>264</td>\n",
       "      <td>返利网</td>\n",
       "      <td>Fanli</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>葛永昌</td>\n",
       "      <td>2007</td>\n",
       "      <td>海纳亚洲、启明创投、乐天</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>264</td>\n",
       "      <td>沪江</td>\n",
       "      <td>Hujiang</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>教育科技</td>\n",
       "      <td>伏彩瑞</td>\n",
       "      <td>2001</td>\n",
       "      <td>汉能投资、软银、顺为资本、海纳亚洲</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>264</td>\n",
       "      <td>麦奇教育科技</td>\n",
       "      <td>iTutorGroup</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>教育科技</td>\n",
       "      <td>杨正大</td>\n",
       "      <td>2008</td>\n",
       "      <td>启明创投、GIC、高盛</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>264</td>\n",
       "      <td>柠萌影业</td>\n",
       "      <td>Linmon</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>苏晓</td>\n",
       "      <td>2014</td>\n",
       "      <td>腾讯、弘毅投资</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>264</td>\n",
       "      <td>出门问问</td>\n",
       "      <td>Mobvoi</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>李志飞</td>\n",
       "      <td>2012</td>\n",
       "      <td>真格基金、红杉资本、海纳亚洲</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>264</td>\n",
       "      <td>毒</td>\n",
       "      <td>Poizon</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>杨冰</td>\n",
       "      <td>2015</td>\n",
       "      <td>DST、虎扑体育、普思资本</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>264</td>\n",
       "      <td>优刻得</td>\n",
       "      <td>UCloud</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>云计算</td>\n",
       "      <td>季昕华</td>\n",
       "      <td>2012</td>\n",
       "      <td>DCM、贝塔斯曼、君联资本</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>264</td>\n",
       "      <td>V领地</td>\n",
       "      <td>V Linker</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>房地产科技</td>\n",
       "      <td>周君强</td>\n",
       "      <td>2011</td>\n",
       "      <td>华平投资</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>264</td>\n",
       "      <td>万能钥匙</td>\n",
       "      <td>WiFi Master key</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>软件与服务</td>\n",
       "      <td>陈大年</td>\n",
       "      <td>2013</td>\n",
       "      <td>海通开元、北极光创投</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>264</td>\n",
       "      <td>壹米滴答</td>\n",
       "      <td>Yimidida</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>物流</td>\n",
       "      <td>杨兴运</td>\n",
       "      <td>2015</td>\n",
       "      <td>博裕资本、厚朴投资、普洛斯、源码资本、鼎晖投资</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>264</td>\n",
       "      <td>洋码头</td>\n",
       "      <td>yMatou</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>曾碧波</td>\n",
       "      <td>2009</td>\n",
       "      <td>远镜创投、赛富基金</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>264</td>\n",
       "      <td>掌门1对1</td>\n",
       "      <td>Zhangmen</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>上海</td>\n",
       "      <td>教育科技</td>\n",
       "      <td>张翼</td>\n",
       "      <td>2014</td>\n",
       "      <td>顺为资本、达晨创投、华平投资</td>\n",
       "      <td>环杭州湾大湾区</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      排名         企业名称                   Company Name  估值（亿人民币）  国家  城市     行业  \\\n",
       "3      6          陆金所                          Lufax      2700  中国  上海   金融科技   \n",
       "13    25       平安医保科技  Ping An Healthcare Technology       600  中国  上海   健康科技   \n",
       "15    34        金融壹账通                     OneConnect       500  中国  上海   金融科技   \n",
       "22    57      达达-京东到家                       New Dada       300  中国  上海     物流   \n",
       "25    57         联影医疗                 United Imaging       300  中国  上海   健康科技   \n",
       "26    57         威马汽车                       WM Motor       300  中国  上海  新能源汽车   \n",
       "31    84         银联商务                      China UMS       200  中国  上海   金融科技   \n",
       "36    84         哈啰出行                      Hellobike       200  中国  上海   共享经济   \n",
       "37    84         复宏汉霖                        Henlius       200  中国  上海   生命科学   \n",
       "38    84         喜马拉雅                       Himalaya       200  中国  上海  媒体和娱乐   \n",
       "48    84          小红书                    Xiaohongshu       200  中国  上海  软件与服务   \n",
       "49    84         易果生鲜                          Yiguo       200  中国  上海   电子商务   \n",
       "51    84         游侠汽车                         Youxia       200  中国  上海  新能源汽车   \n",
       "54   138          爱回收                      Aihuishou       150  中国  上海  软件与服务   \n",
       "59   138           灿星                        Canxing       150  中国  上海  媒体和娱乐   \n",
       "69   138         依图科技                           YITU       150  中国  上海   人工智能   \n",
       "71   224         爱驰汽车                         Aiways       100  中国  上海  新能源汽车   \n",
       "77   224           高顿                         Gaodun       100  中国  上海   教育科技   \n",
       "83   224           界面                        Jiemian       100  中国  上海  媒体和娱乐   \n",
       "86   224          驴妈妈                         lvmama       100  中国  上海   电子商务   \n",
       "88   224         魔方公寓                         Mofang       100  中国  上海  房地产科技   \n",
       "101  224         途虎养车                           Tuhu       100  中国  上海   电子商务   \n",
       "105  224           微鲸                         Whaley       100  中国  上海    消费品   \n",
       "106  224         药明明码                  WuXi NextCODE       100  中国  上海   生命科学   \n",
       "110  224          找钢网                       Zhaogang       100  中国  上海   电子商务   \n",
       "112  264         一起作业                        17zuoye        70  中国  上海   教育科技   \n",
       "118  264         阿里体育                      Alisports        70  中国  上海  媒体和娱乐   \n",
       "119  264         安能物流                            Ane        70  中国  上海     物流   \n",
       "120  264         安翰医疗                          Ankon        70  中国  上海   健康科技   \n",
       "121  264         斑马网络                          Banma        70  中国  上海   人工智能   \n",
       "123  264          波奇网                          Boqii        70  中国  上海   电子商务   \n",
       "129  264         哒哒英语                           DaDa        70  中国  上海   教育科技   \n",
       "131  264          点融网                       Dianrong        70  中国  上海   金融科技   \n",
       "132  264  DotC United                    DotC United        70  中国  上海    大数据   \n",
       "136  264         远景能源                       Envision        70  中国  上海    新能源   \n",
       "138  264          返利网                          Fanli        70  中国  上海   电子商务   \n",
       "148  264           沪江                        Hujiang        70  中国  上海   教育科技   \n",
       "152  264       麦奇教育科技                    iTutorGroup        70  中国  上海   教育科技   \n",
       "166  264         柠萌影业                         Linmon        70  中国  上海  媒体和娱乐   \n",
       "172  264         出门问问                         Mobvoi        70  中国  上海   人工智能   \n",
       "177  264            毒                         Poizon        70  中国  上海   电子商务   \n",
       "187  264          优刻得                         UCloud        70  中国  上海    云计算   \n",
       "189  264          V领地                       V Linker        70  中国  上海  房地产科技   \n",
       "192  264         万能钥匙                WiFi Master key        70  中国  上海  软件与服务   \n",
       "199  264         壹米滴答                       Yimidida        70  中国  上海     物流   \n",
       "200  264          洋码头                         yMatou        70  中国  上海   电子商务   \n",
       "204  264        掌门1对1                       Zhangmen        70  中国  上海   教育科技   \n",
       "\n",
       "    掌门人/创始人  成立年份                     部分投资机构   region  \n",
       "3       计葵生  2011        摩根士丹利、中银集团、国泰君安（香港）  环杭州湾大湾区  \n",
       "13       高菁  2016               IDG、思佰益、软银海外  环杭州湾大湾区  \n",
       "15      叶望春  2015                    IDG、思佰益  环杭州湾大湾区  \n",
       "22      蒯佳祺  2014                红杉资本、DST、京东  环杭州湾大湾区  \n",
       "25       薛敏  2010                  中国人寿、国投创新  环杭州湾大湾区  \n",
       "26       沈晖  2015             远景能源、红杉资本、海纳亚洲  环杭州湾大湾区  \n",
       "31       田林  2002                   光际资本、IDG  环杭州湾大湾区  \n",
       "36       杨磊  2016        纪源资本、磐谷创投、愉悦资本、蚂蚁金服  环杭州湾大湾区  \n",
       "37      刘世高  2009                       华盖资本  环杭州湾大湾区  \n",
       "38      余建军  2012  海纳亚洲、Sierra Ventures、前海兴旺  环杭州湾大湾区  \n",
       "48      毛文超  2013     红杉资本、腾讯、真格基金、纪源资本、阿里巴巴  环杭州湾大湾区  \n",
       "49      金光磊  2005                    阿里巴巴、高盛  环杭州湾大湾区  \n",
       "51       卫俊  2014                  前海梧桐、中创海洋  环杭州湾大湾区  \n",
       "54      陈雪峰  2010     京东、凯辉基金、达晨创投、天图资本、晨兴资本  环杭州湾大湾区  \n",
       "59       田明  2006               阿里巴巴、腾讯、华人文化  环杭州湾大湾区  \n",
       "69       朱珑  2012        云锋基金、红杉资本、真格基金、高瓴资本  环杭州湾大湾区  \n",
       "71       谷峰  2017               腾讯、沙钢集团、明驰基金  环杭州湾大湾区  \n",
       "77       李锋  2006                       前程无忧  环杭州湾大湾区  \n",
       "83       何力  2014                       昆仑信托  环杭州湾大湾区  \n",
       "86      洪清华  2008                  红杉资本、鼎晖投资  环杭州湾大湾区  \n",
       "88       葛岚  2010             中航信托、华平投资、德同资本  环杭州湾大湾区  \n",
       "101      陈敏  2014        高瓴资本、启明创投、君联资本、红杉资本  环杭州湾大湾区  \n",
       "105     黎瑞刚  2015                腾讯、华人文化产业基金  环杭州湾大湾区  \n",
       "106      李革  2015             淡马锡、红杉资本、云锋基金、  环杭州湾大湾区  \n",
       "110      王东  2012         IDG、经纬中国、红杉资本、真格基金  环杭州湾大湾区  \n",
       "112      刘畅  2007             真格基金、顺为资本、老虎基金  环杭州湾大湾区  \n",
       "118      张勇  2015                  云峰基金、太平资产  环杭州湾大湾区  \n",
       "119     王拥军  2010          红杉资本、凯雷投资、高盛、华平投资  环杭州湾大湾区  \n",
       "120     吉朋松  2008                  软银中国、大中投资  环杭州湾大湾区  \n",
       "121      郝飞  2015             国投创新、云锋基金、尚颀资本  环杭州湾大湾区  \n",
       "123     唐颖之  2012               高盛、集富亚洲、点亮资本  环杭州湾大湾区  \n",
       "129      郅慧  2013              老虎基金、华平资本、好未来  环杭州湾大湾区  \n",
       "131     苏海德  2012           老虎环球基金、北极光创投、GIC  环杭州湾大湾区  \n",
       "132      石一  2015             高榕资本、光速中国、晨兴资本  环杭州湾大湾区  \n",
       "136      张雷  2008                       红杉资本  环杭州湾大湾区  \n",
       "138     葛永昌  2007               海纳亚洲、启明创投、乐天  环杭州湾大湾区  \n",
       "148     伏彩瑞  2001          汉能投资、软银、顺为资本、海纳亚洲  环杭州湾大湾区  \n",
       "152     杨正大  2008                启明创投、GIC、高盛  环杭州湾大湾区  \n",
       "166      苏晓  2014                    腾讯、弘毅投资  环杭州湾大湾区  \n",
       "172     李志飞  2012             真格基金、红杉资本、海纳亚洲  环杭州湾大湾区  \n",
       "177      杨冰  2015              DST、虎扑体育、普思资本  环杭州湾大湾区  \n",
       "187     季昕华  2012              DCM、贝塔斯曼、君联资本  环杭州湾大湾区  \n",
       "189     周君强  2011                       华平投资  环杭州湾大湾区  \n",
       "192     陈大年  2013                 海通开元、北极光创投  环杭州湾大湾区  \n",
       "199     杨兴运  2015    博裕资本、厚朴投资、普洛斯、源码资本、鼎晖投资  环杭州湾大湾区  \n",
       "200     曾碧波  2009                  远镜创投、赛富基金  环杭州湾大湾区  \n",
       "204      张翼  2014             顺为资本、达晨创投、华平投资  环杭州湾大湾区  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_上海=df[df['城市'].str.contains('上海')]\n",
    "display(df_上海)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
       "      <th>企业名称</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "      <th>国家</th>\n",
       "      <th>城市</th>\n",
       "      <th>行业</th>\n",
       "      <th>掌门人/创始人</th>\n",
       "      <th>成立年份</th>\n",
       "      <th>部分投资机构</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>57</td>\n",
       "      <td>小鹏汽车</td>\n",
       "      <td>Xpeng Motors</td>\n",
       "      <td>300</td>\n",
       "      <td>中国</td>\n",
       "      <td>广州</td>\n",
       "      <td>新能源汽车</td>\n",
       "      <td>何小鹏</td>\n",
       "      <td>2014</td>\n",
       "      <td>晨兴资本、IDG、经纬中国、顺为资本、阿里巴巴、纪源资本</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>84</td>\n",
       "      <td>云从科技</td>\n",
       "      <td>Cloudwalk</td>\n",
       "      <td>200</td>\n",
       "      <td>中国</td>\n",
       "      <td>广州</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>周曦</td>\n",
       "      <td>2015</td>\n",
       "      <td>顺为资本、元禾原点、前海兴旺</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>138</td>\n",
       "      <td>名创优品</td>\n",
       "      <td>Miniso</td>\n",
       "      <td>150</td>\n",
       "      <td>中国</td>\n",
       "      <td>广州</td>\n",
       "      <td>新零售</td>\n",
       "      <td>叶国富</td>\n",
       "      <td>2013</td>\n",
       "      <td>腾讯、高瓴资本</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>264</td>\n",
       "      <td>碳云智能</td>\n",
       "      <td>Icarbonx</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>广州</td>\n",
       "      <td>健康科技</td>\n",
       "      <td>王俊</td>\n",
       "      <td>2015</td>\n",
       "      <td>天府集团、鑫根资本</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>264</td>\n",
       "      <td>卷皮</td>\n",
       "      <td>Juanpi</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>广州</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>黄承松</td>\n",
       "      <td>2012</td>\n",
       "      <td>天图资本、招银国际、浙江金控</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>264</td>\n",
       "      <td>诺米</td>\n",
       "      <td>Nome</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>广州</td>\n",
       "      <td>新零售</td>\n",
       "      <td>陈浩</td>\n",
       "      <td>2017</td>\n",
       "      <td>红杉资本、华兴资本、天图资本、今日资本</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>264</td>\n",
       "      <td>汇桔网</td>\n",
       "      <td>WTOIP</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>广州</td>\n",
       "      <td>软件与服务</td>\n",
       "      <td>谢旭辉</td>\n",
       "      <td>2013</td>\n",
       "      <td>粤民投、厚朴投资、胡润百富</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>264</td>\n",
       "      <td>要出发</td>\n",
       "      <td>Yaochufa</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>广州</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>丁根芳</td>\n",
       "      <td>2011</td>\n",
       "      <td>众信旅游、红杉资本、创新工场</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      排名  企业名称  Company Name  估值（亿人民币）  国家  城市     行业 掌门人/创始人  成立年份  \\\n",
       "27    57  小鹏汽车  Xpeng Motors       300  中国  广州  新能源汽车     何小鹏  2014   \n",
       "32    84  云从科技     Cloudwalk       200  中国  广州   人工智能      周曦  2015   \n",
       "64   138  名创优品        Miniso       150  中国  广州    新零售     叶国富  2013   \n",
       "149  264  碳云智能      Icarbonx        70  中国  广州   健康科技      王俊  2015   \n",
       "155  264    卷皮        Juanpi        70  中国  广州   电子商务     黄承松  2012   \n",
       "174  264    诺米          Nome        70  中国  广州    新零售      陈浩  2017   \n",
       "194  264   汇桔网         WTOIP        70  中国  广州  软件与服务     谢旭辉  2013   \n",
       "195  264   要出发      Yaochufa        70  中国  广州   电子商务     丁根芳  2011   \n",
       "\n",
       "                           部分投资机构  region  \n",
       "27   晨兴资本、IDG、经纬中国、顺为资本、阿里巴巴、纪源资本  粤港澳大湾区  \n",
       "32                 顺为资本、元禾原点、前海兴旺  粤港澳大湾区  \n",
       "64                        腾讯、高瓴资本  粤港澳大湾区  \n",
       "149                     天府集团、鑫根资本  粤港澳大湾区  \n",
       "155                天图资本、招银国际、浙江金控  粤港澳大湾区  \n",
       "174           红杉资本、华兴资本、天图资本、今日资本  粤港澳大湾区  \n",
       "194                 粤民投、厚朴投资、胡润百富  粤港澳大湾区  \n",
       "195                众信旅游、红杉资本、创新工场  粤港澳大湾区  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_广州=df[df['城市'].str.contains('广州')]\n",
    "display(df_广州)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>排名</th>\n",
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       "      <th>Company Name</th>\n",
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       "      <th>国家</th>\n",
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       "      <th>行业</th>\n",
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       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>微众银行</td>\n",
       "      <td>WeBank</td>\n",
       "      <td>1500</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>顾敏</td>\n",
       "      <td>2014</td>\n",
       "      <td>腾讯、华平投资、淡马锡</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15</td>\n",
       "      <td>大疆</td>\n",
       "      <td>DJI</td>\n",
       "      <td>1000</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>机器人</td>\n",
       "      <td>汪滔</td>\n",
       "      <td>2006</td>\n",
       "      <td>Accel、红杉资本、麦星投资</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>57</td>\n",
       "      <td>柔宇科技</td>\n",
       "      <td>Royole</td>\n",
       "      <td>300</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>消费品</td>\n",
       "      <td>刘自鸿</td>\n",
       "      <td>2012</td>\n",
       "      <td>中信产业基金、基石资本、IDG</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>57</td>\n",
       "      <td>优必选</td>\n",
       "      <td>Ubtech</td>\n",
       "      <td>300</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>机器人</td>\n",
       "      <td>周剑</td>\n",
       "      <td>2012</td>\n",
       "      <td>启明创投、科大讯飞、鼎晖投资、腾讯</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>84</td>\n",
       "      <td>大地影院</td>\n",
       "      <td>Dadi Digital Cinema</td>\n",
       "      <td>200</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>刘荣</td>\n",
       "      <td>2006</td>\n",
       "      <td>阿里影业</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>84</td>\n",
       "      <td>跨越速运</td>\n",
       "      <td>Kuayue Express</td>\n",
       "      <td>200</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>物流</td>\n",
       "      <td>胡永</td>\n",
       "      <td>2007</td>\n",
       "      <td>红杉资本</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>138</td>\n",
       "      <td>雾芯科技</td>\n",
       "      <td>RELX</td>\n",
       "      <td>150</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>消费品</td>\n",
       "      <td>汪莹</td>\n",
       "      <td>2018</td>\n",
       "      <td>IDG、源码资本、红杉资本</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>224</td>\n",
       "      <td>全棉时代</td>\n",
       "      <td>PurCotton</td>\n",
       "      <td>100</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>健康科技</td>\n",
       "      <td>李建全</td>\n",
       "      <td>2009</td>\n",
       "      <td>红杉资本</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>224</td>\n",
       "      <td>SheIn</td>\n",
       "      <td>SheIn</td>\n",
       "      <td>100</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>电子商务</td>\n",
       "      <td>许仰天</td>\n",
       "      <td>2008</td>\n",
       "      <td>JAFC、IDG、景林资本、红杉资本</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>224</td>\n",
       "      <td>土巴兔</td>\n",
       "      <td>Tubatu</td>\n",
       "      <td>100</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>软件与服务</td>\n",
       "      <td>王国彬</td>\n",
       "      <td>2008</td>\n",
       "      <td>红杉资本、经纬中国、58同城</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>264</td>\n",
       "      <td>岩心科技</td>\n",
       "      <td>Akulaku</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>李文博</td>\n",
       "      <td>2015</td>\n",
       "      <td>启明创投、蚂蚁金服</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>264</td>\n",
       "      <td>房多多</td>\n",
       "      <td>FangDD</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>房地产科技</td>\n",
       "      <td>段毅</td>\n",
       "      <td>2011</td>\n",
       "      <td>嘉御基金、光速中国、鼎晖投资</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>264</td>\n",
       "      <td>丰巢科技</td>\n",
       "      <td>Fcbox</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>物流</td>\n",
       "      <td>徐育斌</td>\n",
       "      <td>2015</td>\n",
       "      <td>顺丰速运、鼎晖投资、国开金融</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>264</td>\n",
       "      <td>辣妈帮</td>\n",
       "      <td>Lamabang</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>健康科技</td>\n",
       "      <td>金赞</td>\n",
       "      <td>2011</td>\n",
       "      <td>经纬中国、晨兴资本</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>264</td>\n",
       "      <td>联易融</td>\n",
       "      <td>Linklogis</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>宋群</td>\n",
       "      <td>2016</td>\n",
       "      <td>腾讯、泛海投资、中信资本</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>264</td>\n",
       "      <td>奥比中光</td>\n",
       "      <td>Orbbec</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>人工智能</td>\n",
       "      <td>黄源浩</td>\n",
       "      <td>2013</td>\n",
       "      <td>蚂蚁金服、赛富投资、松禾资本</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>264</td>\n",
       "      <td>我来贷</td>\n",
       "      <td>WeLab</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>金融科技</td>\n",
       "      <td>谢飞</td>\n",
       "      <td>2014</td>\n",
       "      <td>阿里巴巴、淡马锡、红杉资本</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>264</td>\n",
       "      <td>越海全球</td>\n",
       "      <td>YH Global</td>\n",
       "      <td>70</td>\n",
       "      <td>中国</td>\n",
       "      <td>深圳</td>\n",
       "      <td>物流</td>\n",
       "      <td>张泉</td>\n",
       "      <td>2012</td>\n",
       "      <td>涌铧投资、汇能金融、磐石资本</td>\n",
       "      <td>粤港澳大湾区</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      排名   企业名称         Company Name  估值（亿人民币）  国家  城市     行业 掌门人/创始人  成立年份  \\\n",
       "4     11   微众银行               WeBank      1500  中国  深圳   金融科技      顾敏  2014   \n",
       "8     15     大疆                  DJI      1000  中国  深圳    机器人      汪滔  2006   \n",
       "23    57   柔宇科技               Royole       300  中国  深圳    消费品     刘自鸿  2012   \n",
       "24    57    优必选               Ubtech       300  中国  深圳    机器人      周剑  2012   \n",
       "33    84   大地影院  Dadi Digital Cinema       200  中国  深圳  媒体和娱乐      刘荣  2006   \n",
       "41    84   跨越速运       Kuayue Express       200  中国  深圳     物流      胡永  2007   \n",
       "66   138   雾芯科技                 RELX       150  中国  深圳    消费品      汪莹  2018   \n",
       "93   224   全棉时代            PurCotton       100  中国  深圳   健康科技     李建全  2009   \n",
       "95   224  SheIn                SheIn       100  中国  深圳   电子商务     许仰天  2008   \n",
       "100  224    土巴兔               Tubatu       100  中国  深圳  软件与服务     王国彬  2008   \n",
       "117  264   岩心科技              Akulaku        70  中国  深圳   金融科技     李文博  2015   \n",
       "137  264    房多多               FangDD        70  中国  深圳  房地产科技      段毅  2011   \n",
       "139  264   丰巢科技                Fcbox        70  中国  深圳     物流     徐育斌  2015   \n",
       "161  264    辣妈帮             Lamabang        70  中国  深圳   健康科技      金赞  2011   \n",
       "165  264    联易融            Linklogis        70  中国  深圳   金融科技      宋群  2016   \n",
       "176  264   奥比中光               Orbbec        70  中国  深圳   人工智能     黄源浩  2013   \n",
       "191  264    我来贷                WeLab        70  中国  深圳   金融科技      谢飞  2014   \n",
       "196  264   越海全球            YH Global        70  中国  深圳     物流      张泉  2012   \n",
       "\n",
       "                 部分投资机构  region  \n",
       "4           腾讯、华平投资、淡马锡  粤港澳大湾区  \n",
       "8       Accel、红杉资本、麦星投资  粤港澳大湾区  \n",
       "23      中信产业基金、基石资本、IDG  粤港澳大湾区  \n",
       "24    启明创投、科大讯飞、鼎晖投资、腾讯  粤港澳大湾区  \n",
       "33                 阿里影业  粤港澳大湾区  \n",
       "41                 红杉资本  粤港澳大湾区  \n",
       "66        IDG、源码资本、红杉资本  粤港澳大湾区  \n",
       "93                 红杉资本  粤港澳大湾区  \n",
       "95   JAFC、IDG、景林资本、红杉资本  粤港澳大湾区  \n",
       "100      红杉资本、经纬中国、58同城  粤港澳大湾区  \n",
       "117           启明创投、蚂蚁金服  粤港澳大湾区  \n",
       "137      嘉御基金、光速中国、鼎晖投资  粤港澳大湾区  \n",
       "139      顺丰速运、鼎晖投资、国开金融  粤港澳大湾区  \n",
       "161           经纬中国、晨兴资本  粤港澳大湾区  \n",
       "165        腾讯、泛海投资、中信资本  粤港澳大湾区  \n",
       "176      蚂蚁金服、赛富投资、松禾资本  粤港澳大湾区  \n",
       "191       阿里巴巴、淡马锡、红杉资本  粤港澳大湾区  \n",
       "196      涌铧投资、汇能金融、磐石资本  粤港澳大湾区  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_深圳=df[df['城市'].str.contains('深圳')]\n",
    "display(df_深圳)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据统计计算\n",
    "* 计算估值总和以及均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22230"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "8990"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "4510"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_北京各企业估值总和=df_北京['估值（亿人民币）'].sum()\n",
    "df_上海各企业估值总和=df_上海['估值（亿人民币）'].sum()\n",
    "df_广州各企业估值总和=df_广州['估值（亿人民币）'].sum()\n",
    "df_深圳各企业估值总和=df_深圳['估值（亿人民币）'].sum()\n",
    "display(df_北京各企业估值总和)\n",
    "display(df_上海各企业估值总和)\n",
    "display(df_广州各企业估值总和)\n",
    "display(df_深圳各企业估值总和)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>北京各企业估值总和</th>\n",
       "      <th>上海各企业估值总和</th>\n",
       "      <th>广州各企业估值总和</th>\n",
       "      <th>深圳各企业估值总和</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22230</td>\n",
       "      <td>8990</td>\n",
       "      <td>1000</td>\n",
       "      <td>4510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   北京各企业估值总和  上海各企业估值总和  广州各企业估值总和  深圳各企业估值总和\n",
       "0      22230       8990       1000       4510"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df估值总和=pd.DataFrame({'北京各企业估值总和':df_北京['估值（亿人民币）'].sum(),'上海各企业估值总和':df_上海['估值（亿人民币）'].sum(),'广州各企业估值总和':df_广州['估值（亿人民币）'].sum(),'深圳各企业估值总和':df_深圳['估值（亿人民币）'].sum()},index=[0])\n",
    "df估值总和"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "271.0975609756098"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "191.27659574468086"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "125.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "250.55555555555554"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_北京各企业估值均值=df_北京['估值（亿人民币）'].mean()\n",
    "df_上海各企业估值均值=df_上海['估值（亿人民币）'].mean()\n",
    "df_广州各企业估值均值=df_广州['估值（亿人民币）'].mean()\n",
    "df_深圳各企业估值均值=df_深圳['估值（亿人民币）'].mean()\n",
    "display(df_北京各企业估值均值)\n",
    "display(df_上海各企业估值均值)\n",
    "display(df_广州各企业估值均值)\n",
    "display(df_深圳各企业估值均值)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 重组数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>北京各企业估值均值</th>\n",
       "      <th>上海各企业估值均值</th>\n",
       "      <th>广州各企业估值均值</th>\n",
       "      <th>深圳各企业估值均值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>271.097561</td>\n",
       "      <td>191.276596</td>\n",
       "      <td>125.0</td>\n",
       "      <td>250.555556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    北京各企业估值均值   上海各企业估值均值  广州各企业估值均值   深圳各企业估值均值\n",
       "0  271.097561  191.276596      125.0  250.555556"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df估值均值=pd.DataFrame({'北京各企业估值均值':df_北京['估值（亿人民币）'].mean(),'上海各企业估值均值':df_上海['估值（亿人民币）'].mean(),'广州各企业估值均值':df_广州['估值（亿人民币）'].mean(),'深圳各企业估值均值':df_深圳['估值（亿人民币）'].mean()},index=[0])\n",
    "df估值均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>北上广深企业估值</th>\n",
       "      <th>值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京各企业估值总和</td>\n",
       "      <td>22230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海各企业估值总和</td>\n",
       "      <td>8990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>广州各企业估值总和</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>深圳各企业估值总和</td>\n",
       "      <td>4510</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    北上广深企业估值      值\n",
       "0  北京各企业估值总和  22230\n",
       "1  上海各企业估值总和   8990\n",
       "2  广州各企业估值总和   1000\n",
       "3  深圳各企业估值总和   4510"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df合并=pd.DataFrame({'北上广深企业估值':['北京各企业估值总和','上海各企业估值总和','广州各企业估值总和','深圳各企业估值总和'],'值':[df_北京['估值（亿人民币）'].sum(),df_上海['估值（亿人民币）'].sum(),df_广州['估值（亿人民币）'].sum(),df_深圳['估值（亿人民币）'].sum()]})\n",
    "df合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>北上广深企业估值</th>\n",
       "      <th>值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京各企业估值均值</td>\n",
       "      <td>271.097561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海各企业估值均值</td>\n",
       "      <td>191.276596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>广州各企业估值均值</td>\n",
       "      <td>125.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>深圳各企业估值均值</td>\n",
       "      <td>250.555556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    北上广深企业估值           值\n",
       "0  北京各企业估值均值  271.097561\n",
       "1  上海各企业估值均值  191.276596\n",
       "2  广州各企业估值均值  125.000000\n",
       "3  深圳各企业估值均值  250.555556"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df合并2=pd.DataFrame({'北上广深企业估值':['北京各企业估值均值','上海各企业估值均值','广州各企业估值均值','深圳各企业估值均值'],'值':[df_北京['估值（亿人民币）'].mean(),df_上海['估值（亿人民币）'].mean(),df_广州['估值（亿人民币）'].mean(),df_深圳['估值（亿人民币）'].mean()]})\n",
    "df合并2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df合并2=pd.DataFrame({'北京各行业估值总和':df_北京['估值（亿人民币）'].sum(),'上海各行业估值总和':df_上海['估值（亿人民币）'].sum(),'广州各行业估值总和':df_广州['估值（亿人民币）'].sum(),'深圳各行业估值总和':df_深圳['估值（亿人民币）'].sum(),'北京各行业估值均值':df_北京['估值（亿人民币）'].mean(),'上海各行业估值均值':df_上海['估值（亿人民币）'].mean(),'广州各行业估值均值':df_广州['估值（亿人民币）'].mean(),'深圳各行业估值均值':df_深圳['估值（亿人民币）'].mean()},index=[0])\n",
    "#df合并2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据可视化图表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### “北上广深”四个城市的独角兽企业估值总和 对比图\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'D:\\\\APP\\\\anaconda\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #解决中文乱码\n",
    "x=df合并['北上广深企业估值']\n",
    "height=df合并['值']\n",
    "plt.grid(axis=\"y\", which=\"major\")  # 生成虚线网格\n",
    "#x、y轴标签\n",
    "plt.xlabel('指标')\n",
    "plt.ylabel('估值（亿人民币）')\n",
    "#图表标题\n",
    "plt.title('北上广深估值对比')\n",
    "plt.bar(x,height,width = 0.5,align='center',color = 'b',alpha=0.5,bottom=0.8)\n",
    "#设置每个柱子的文本标签,format(b,',')格式化销售额为千位分隔符格式\n",
    "for a,b in zip(x,height):\n",
    "    plt.text(a, b,format(b,','), ha='center', va= 'bottom',fontsize=15,color = 'k',alpha=0.9)\n",
    "#图例\n",
    "plt.legend(['值'])\n",
    "plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### “北上广深”四个城市的独角兽企业估值均值 对比图\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'matplotlib.pyplot' from 'D:\\\\APP\\\\anaconda\\\\lib\\\\site-packages\\\\matplotlib\\\\pyplot.py'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #解决中文乱码\n",
    "x=df合并2['北上广深企业估值']\n",
    "height=df合并2['值']\n",
    "plt.grid(axis=\"y\", which=\"major\")  # 生成虚线网格\n",
    "#x、y轴标签\n",
    "plt.xlabel('指标')\n",
    "plt.ylabel('估值（亿人民币）')\n",
    "#图表标题\n",
    "plt.title('北上广深估值对比')\n",
    "plt.bar(x,height,width = 0.5,align='center',color = 'r',alpha=0.5,bottom=0.8)\n",
    "#设置每个柱子的文本标签,format(b,',')格式化销售额为千位分隔符格式\n",
    "for a,b in zip(x,height):\n",
    "    plt.text(a, b,format(b,','), ha='center', va= 'bottom',fontsize=15,color = 'k',alpha=0.9)\n",
    "#图例\n",
    "plt.legend(['值'])\n",
    "plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据分组聚合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_行业 = df.groupby(['行业','企业名称']).sum()\n",
    "# df_行业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_估值 = df_行业.groupby('行业').agg({'估值（亿人民币）':['sum', 'mean']})\n",
    "# df_估值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_估值 = df_行业.groupby('行业').mean()\n",
    "# df_估值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>行业</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>6890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>共享经济</td>\n",
       "      <td>4040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>电子商务</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>金融科技</td>\n",
       "      <td>1920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>人工智能</td>\n",
       "      <td>1430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       行业  估值（亿人民币）\n",
       "6   媒体和娱乐      6890\n",
       "3    共享经济      4040\n",
       "14   电子商务      2300\n",
       "17   金融科技      1920\n",
       "1    人工智能      1430"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_北京行业估值=df_北京.groupby('行业').sum().drop(['成立年份','排名'],axis=1).reset_index()\n",
    "df_北京行业 = df_北京行业估值.sort_values(by='估值（亿人民币）',ascending=False).head(n=5)\n",
    "df_北京行业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>行业</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>金融科技</td>\n",
       "      <td>3470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>健康科技</td>\n",
       "      <td>970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>电子商务</td>\n",
       "      <td>780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>新能源汽车</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       行业  估值（亿人民币）\n",
       "15   金融科技      3470\n",
       "2    健康科技       970\n",
       "13   电子商务       780\n",
       "9   新能源汽车       600\n",
       "5   媒体和娱乐       590"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_上海行业估值=df_上海.groupby('行业').sum().drop(['成立年份','排名'],axis=1).reset_index()\n",
    "df_上海行业 = df_上海行业估值.sort_values(by='估值（亿人民币）',ascending=False).head(n=5)\n",
    "df_上海行业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>行业</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>新能源汽车</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>新零售</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>人工智能</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>电子商务</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>健康科技</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      行业  估值（亿人民币）\n",
       "2  新能源汽车       300\n",
       "3    新零售       220\n",
       "0   人工智能       200\n",
       "4   电子商务       140\n",
       "1   健康科技        70"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_广州行业估值=df_广州.groupby('行业').sum().drop(['成立年份','排名'],axis=1).reset_index()\n",
    "df_广州行业 = df_广州行业估值.sort_values(by='估值（亿人民币）',ascending=False).head(n=5)\n",
    "df_广州行业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>行业</th>\n",
       "      <th>估值（亿人民币）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>金融科技</td>\n",
       "      <td>1710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>机器人</td>\n",
       "      <td>1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>消费品</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>物流</td>\n",
       "      <td>340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>媒体和娱乐</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      行业  估值（亿人民币）\n",
       "9   金融科技      1710\n",
       "4    机器人      1300\n",
       "5    消费品       450\n",
       "6     物流       340\n",
       "2  媒体和娱乐       200"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_深圳行业估值=df_深圳.groupby('行业').sum().drop(['成立年份','排名'],axis=1).reset_index()\n",
    "df_深圳行业 = df_深圳行业估值.sort_values(by='估值（亿人民币）',ascending=False).head(n=5)\n",
    "df_深圳行业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import cufflinks as cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyplt = py.offline.plot\n",
    "labels = df_北京行业['行业']\n",
    "values = df_北京行业['估值（亿人民币）']\n",
    "trace = [go.Pie(labels=labels, values=values)]\n",
    "layout = go.Layout(title = '北京前五个行业占比',)\n",
    "fig = go.Figure(data = trace, layout = layout)\n",
    "py.offline.plot(fig, filename=\"example北京.html\",auto_open=False)\n",
    "with open(\"example北京.html\", encoding=\"utf8\", mode=\"r\") as f:\n",
    "     plot_all3 = \"\".join(f.readlines())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyplt = py.offline.plot\n",
    "labels = df_上海行业['行业']\n",
    "values = df_上海行业['估值（亿人民币）']\n",
    "trace = [go.Pie(labels=labels, values=values)]\n",
    "layout = go.Layout(title = '上海前五个行业占比',)\n",
    "fig = go.Figure(data = trace, layout = layout)\n",
    "py.offline.plot(fig, filename=\"example上海.html\",auto_open=False)\n",
    "with open(\"example上海.html\", encoding=\"utf8\", mode=\"r\") as f:\n",
    "     plot_all4 = \"\".join(f.readlines())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyplt = py.offline.plot\n",
    "labels = df_广州行业['行业']\n",
    "values = df_广州行业['估值（亿人民币）']\n",
    "trace = [go.Pie(labels=labels, values=values)]\n",
    "layout = go.Layout(title = '广州前五个行业占比',)\n",
    "fig = go.Figure(data = trace, layout = layout)\n",
    "py.offline.plot(fig, filename=\"example广州.html\",auto_open=False)\n",
    "with open(\"example广州.html\", encoding=\"utf8\", mode=\"r\") as f:\n",
    "     plot_all5 = \"\".join(f.readlines())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "pyplt = py.offline.plot\n",
    "labels = df_深圳行业['行业']\n",
    "values = df_深圳行业['估值（亿人民币）']\n",
    "trace = [go.Pie(labels=labels, values=values)]\n",
    "layout = go.Layout(title = '深圳前五个行业占比',)\n",
    "fig = go.Figure(data = trace, layout = layout)\n",
    "py.offline.plot(fig, filename=\"example深圳.html\",auto_open=False)\n",
    "with open(\"example深圳.html\", encoding=\"utf8\", mode=\"r\") as f:\n",
    "     plot_all6 = \"\".join(f.readlines())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据网站展示出来（Flask）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据说明（数据科学文档）\n",
    "* a.介绍你可以展示的数据有哪些？\n",
    "* b.数据的说明、结论和价值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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